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03/23/2023
Three ways to improve feature alignment for open vocabulary detection
The core problem in zero-shot open vocabulary detection is how to align ...
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11/08/2022
Self-conditioned Embedding Diffusion for Text Generation
Can continuous diffusion models bring the same performance breakthrough ...
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10/09/2022
Dissecting adaptive methods in GANs
Adaptive methods are a crucial component widely used for training genera...
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04/29/2022
Flamingo: a Visual Language Model for Few-Shot Learning
Building models that can be rapidly adapted to numerous tasks using only...
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03/29/2022
Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training ...
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02/02/2022
Unified Scaling Laws for Routed Language Models
The performance of a language model has been shown to be effectively mod...
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12/08/2021
Improving language models by retrieving from trillions of tokens
We enhance auto-regressive language models by conditioning on document c...
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10/16/2020
Differentiable Divergences Between Time Series
Computing the discrepancy between time series of variable sizes is notor...
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03/05/2020
Fine-grain atlases of functional modes for fMRI analysis
Population imaging markedly increased the size of functional-imaging dat...
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03/03/2020
Online Sinkhorn: optimal transportation distances from sample streams
Optimal Transport (OT) distances are now routinely used as loss function...
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02/14/2020
A mean-field analysis of two-player zero-sum games
Finding Nash equilibria in two-player zero-sum continuous games is a cen...
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05/29/2019
Extra-gradient with player sampling for provable fast convergence in n-player games
Data-driven model training is increasingly relying on finding Nash equil...
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05/15/2019
Geometric Losses for Distributional Learning
Building upon recent advances in entropy-regularized optimal transport, ...
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09/17/2018
Extracting Universal Representations of Cognition across Brain-Imaging Studies
The size of publicly available data in cognitive neuro-imaging has incre...
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02/11/2018
Differentiable Dynamic Programming for Structured Prediction and Attention
Dynamic programming (DP) solves a variety of structured combinatorial pr...
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10/31/2017
Learning Neural Representations of Human Cognition across Many fMRI Studies
Cognitive neuroscience is enjoying rapid increase in extensive public br...
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01/19/2017
Stochastic Subsampling for Factorizing Huge Matrices
We present a matrix-factorization algorithm that scales to input matrice...
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05/03/2016
Dictionary Learning for Massive Matrix Factorization
Sparse matrix factorization is a popular tool to obtain interpretable da...
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02/08/2016